StatPilot

Description: This is my final project for CS50x course. It's an interactive web application for statistical data analysis, machine learning modeling, and model explainability — built with Flask and Python.

Features

• 📊 Data Analysis: Upload a CSV and get automatic descriptive statistics, visualizations, and insights.
• 🧠 Modeling: Train ML models (e.g. regression, classification) directly from your browser.
• 💡 Explainability: Understand your model decisions with techniques like feature importance and SHAP values.

Technologies

• Python, Flask
• Pandas, Scikit-learn, Matplotlib, Seaborn, NLTK, Wordcloud, shap
• Jinja2 for templating
• HTML/CSS(+Tailwind)/JS for frontend
• Figma for logo design

Project Structure

/project-root
│
├── app.py      # Main Flask app
├── templates/  # HTML pages
├── static/     # CSS, JS, temp folder for datasets/plots
├── modules/ 
│ ├── analysis.py
│ ├── modeling.py
│ └── explainability.py
└── README.md

User Interface

Access the app here

No installation required — just open the app and start exploring your data. Due to render free limitations, it could take a few minutes to load. VIDEO DEMO (for course submission): https://youtu.be/deRl0LRDJKQ

Usage

1. Navigate to the "Data Analysis" section to upload your dataset (CSV format) to view automatic summaries, statistics, and visualizations.
2. Use the "Machine Learning" tab to train a machine learning model on your data.
3. Go to "Explainable AI" to interpret model decisions with visual tools.

Notes

• Your data is not stored or shared.
• Categorical, numerical, and textual data are automatically detected and analyzed accordingly.
• Ensure your data are clean and correctly decoded to help the tool doing proper analysis.
• At the moment the app does not support time series analysis and all datetime columns are dropped. Coming soon!

License

MIT License. Feel free to use, fork, and modify the project.